• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.137-137
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• 2023

해양쓰레기는 해안의 심미적 가치 저하뿐만 아니라 생태계 파괴, 유령 어업에 따른 수산업 피해 등의 사회적·환경적 문제를 발생시키며, 그중 70% 이상은 육상 기인으로 플라스틱 및 기타 쓰레기가 주를 이루는 해외와 달리 국내의 경우 다량의 초목류를 포함하고 있다. 다양한 부유쓰레기에 대한 기존의 해양쓰레기량 추정의 한계와 하천·하구 쓰레기 수거의 효율화를 위해 해양으로 유입되는 부유쓰레기 방지를 위한 실효성 있는 대책 수립이 필요한 실정이다. 본 연구는 해양 유입 전 하천의 차단시설에 차집된 부유쓰레기의 수거 효율화 및 지속가능한 해양쓰레기 데이터 구축을 위해 AI기반의 기술을 통해 부유쓰레기 성상 분석 기법(Object Detection)과 차집량 분석 기법(Semantic Segmentation)을 활용하였다. 실제와 유사한 데이터 수집을 위해 다양한 하천 환경(정수조, 소하천, 급경사수로)에 대해 탁도(녹조, 유사), 광량, 쓰레기형상, 초목류 함량, 날씨(소하천), 유속(급경사수로) 등의 실험조건에 대하여 해양쓰레기 분류 기준 및 통계를 바탕으로 부유쓰레기 종류 선정하여 학습을 위한 데이터를 수집하였다. 학습 목적에 따라 구분하여 라벨링(Bounding box, Polygon)을 수행하고, 각 분석 기법별 전이학습을 통해 Phase 1(정수조), Phase 2(소하천), Phase 3(급경사수로) 순서로 모델을 고도화하였다. 성상 분석을 위해 YOLO v4를 활용하여 Train, Test DataSet(9:1)을 구성하고 학습 및 평가는 Iteration마다의 mAP, loss 값을 통해 비교하였으며, 학습 Phase에 따라 모델 고도화로 Test Set의 mAP 값이 성상별로 높아짐을 확인하였으며, 차집량 분석을 위해 Unet을 활용하여 Train, Test, Validation DataSet(8.5:1:0.5)을 구성하고 epoch별 IoU(intersection over Union), F1-score, loss 값을 비교하여 정성적, 정량적 평가 모두 Phase 3에서 가장 높은 성능을 확인하였다. 향후 하천 환경에서의 다양한 영양인자별 분석을 통해 주요 영향인자 도출 및 Hyper Parameter 최적화를 통한 모델 고도화로 인해 활용성이 높아질 것으로 판단된다.

• Journal of Ocean Engineering and Technology
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• v.36 no.6
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• pp.380-389
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• 2022

In the Haeundae area of Busan, South Korea, damage has continued to occur recently from building wind from caused by dense skyscrapers. Five wind observation stations were installed near LCT residential towers in Haeundae to analyze the effect of building winds during typhoon Omais. The impact of building wind was analyzed through relative and absolute evaluations. At an intersection located southeast of LCT (L-2), the strongest wind speed was measured during the monitoring. The maximum average wind speed for one minute was observed to be 38.93 m/s, which is about three times stronger than at an ocean observation buoy (12.7 m/s) at the same time. It is expected that 3 to 4 times stronger wind can be induced under certain conditions compared to the surrounding areas due to the building wind effect. In a Beaufort wind scale analysis, the wind speed at an ocean observatory was mostly distributed at Beaufort number 4, and the maximum was 8. At L-2, more than 50% of the wind speed exceeded Beaufort number 4, and numbers up to 12 were observed. However, since actual measurement has a limitation in analyzing the entire range, cross-validation with computational fluid dynamics simulation data is required to understand the characteristics of building winds.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.949-965
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• 2023

In this study, we propose a method to monitor the surface area of agricultural reservoirs in South Korea using Sentinel-1 synthetic aperture radar images and the deep learning model, Swin Transformer. Utilizing the Google Earth Engine platform, datasets from 2017 to 2021 were constructed for seven agricultural reservoirs, categorized into 700 K-ton, 900 K-ton, and 1.5 M-ton capacities. For four of the reservoirs, a total of 1,283 images were used for model training through shuffling and 5-fold cross-validation techniques. Upon evaluation, the Swin Transformer Large model, configured with a window size of 12, demonstrated superior semantic segmentation performance, showing an average accuracy of 99.54% and a mean intersection over union (mIoU) of 95.15% for all folds. When the best-performing model was applied to the datasets of the remaining three reservoirsfor validation, it achieved an accuracy of over 99% and mIoU of over 94% for all reservoirs. These results indicate that the Swin Transformer model can effectively monitor the surface area of agricultural reservoirs in South Korea.

• Journal of Korean Society of Transportation
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• v.32 no.6
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• pp.567-578
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• 2014

The study suggested a method to improve analysis accuracy such that the interactive effects of transportation changes between outside and inside of sub-areas were sequentially considered in the analysis by linking a macroscopic network analysis and a microscopic traffic simulation. A dynamic O/D estimation method was developed for practical implement of sub-area microscopic simulation analysis by using the results of macroscopic network analysis, the results of selected link analysis at the cordon line of the sub-area, departure time data of household travel survey, timely observed traffic volume data at the cordon. This estimated dynamic O/D for the sub-area made it possible to analyze traffic phenomena in details. Various detailed phenomena such as traffic queues, delay at intersection, and conflicts between vehicles, which is impossible to be grasped through a macroscopic analysis, can be analyzed with the dynamic microscopic traffic analysis. Through implementing an empirical study and validation, the study provided a reference result about accuracy of a microscopic traffic simulation of a sub-area to help its application for real transportation policy analysis.

• Journal of Korean Society of Transportation
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• v.32 no.3
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• pp.266-279
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• 2014

For the evaluation of roadway safety, diverse methods, including before-after studies, simple comparison using historic traffic accident data, methods based on experts' opinion or literature, have been applied. Especially, many research efforts have developed traffic accident prediction models in order to identify critical elements causing accidents and evaluate the level of safety. A traffic accident prediction model must secure predictability and transferability. By acquiring the predictability, the model can increase the accuracy in predicting the frequency of accidents qualitatively and quantitatively. By guaranteeing the transferability, the model can be used for other locations with acceptable accuracy. To this end, traffic accident prediction models using non-linear regression, artificial neural network, and structural equation were developed in this study. The predictability and transferability of three models were compared using a model development data set collected from 90 signalized intersections and a model validation data set from other 33 signalized intersections based on mean absolute deviation and mean squared prediction error. As a result of the comparison using the model development data set, the artificial neural network showed the highest predictability. However, the non-linear regression model was found out to be most appropriate in the comparison using the model validation data set. Conclusively, the artificial neural network has a strong ability in representing the relationship between the frequency of traffic accidents and traffic and road design elements. However, the predictability of the artificial neural network significantly decreased when the artificial neural network was applied to a new data which was not used in the model developing.